A study on early diagnosis for fracture non-union prediction using deep learning and bone morphometric parameters
This study proposed the VM-TE-UNet for fracture areas segmentation, extracted micro-CT indices, and established an early diagnostic model for fracture non-union. We believe that the prediction model can effectively screen out samples of poor fracture rehabilitation caused by blood supply limitations in rats 14 days after fracture, rather than the widely accepted 35 or 40 days. This provides important reference for the clinical prediction of fracture non-union and early intervention treatment.
Authors
- Guangpu Wang
- Jing Zhao (ORCID: https://orcid.org/0000-0003-2520-5435)
- Xiaoman Dong
- Jinglai Sun (ORCID: https://orcid.org/0000-0003-3683-1968)
- Zhi Wang (ORCID: https://orcid.org/0000-0001-6087-8972)
- Yu Guo (ORCID: https://orcid.org/0000-0001-7686-3775)
- Xianghong Meng (ORCID: https://orcid.org/0000-0003-2470-7046)
- Shuo Wang (ORCID: https://orcid.org/0000-0001-7851-3824)
- Hui Yu (ORCID: https://orcid.org/0000-0002-6644-550X)
- Qingsong Wang (ORCID: https://orcid.org/0000-0001-5858-3622)
- Qi Mu (ORCID: https://orcid.org/0009-0005-7534-5999)
Institutions
- Tianjin University (CN)
- Tianjin Hospital (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-03-24
- DOI
- https://doi.org/10.3389/fmed.2025.1547588
- Citations
- 4
- Primary Topic
- Bone fractures and treatments
- Type
- article
- Field-Weighted Citation Impact
- 5.42